REVIEW 3 cited by
Notes on asymptotics of sample eigenstructure for spiked covariance models with non-Gaussian data
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
These expository notes serve as a reference for an accompanying post Morales-Jimenez et al. [2018]. In the spiked covariance model, we develop results on asymptotic normality of sample leading eigenvalues and certain projections of the corresponding sample eigenvectors. The results parallel those of Paul [2007], but are given using the non-Gaussian model of Bai and Yao [2008]. The results are not new, and citations are given, but proofs are collected and organized as a point of departure for Morales-Jimenez et al. [2018].
Forward citations
Cited by 3 Pith papers
-
Limiting eigen-structure of spiked sample covariance matrices under missing observations
The top eigenvalues and eigenvectors of sample covariance matrices under entrywise missing data follow Gaussian limits whose parameters depend on the missingness probabilities.
-
Estimation of the number of principal components in high-dimensional multivariate extremes
AIC and BIC rules for the number of significant principal components in multivariate extremes are developed and shown to be weakly consistent under a spiked covariance model.
-
High-dimensional ridgeless least squares interpolation under spiked covariance structures
For ridgeless least squares with a spiked covariance, the asymptotic prediction risk is a closed-form function of spike eigenvalues, the aspect ratio, and target-spike alignment.
Discussion (0). Continue with ORCID to comment.